--- title: "Put the Model in the Basement" description: "Could Swiss providers sell local K3-class inference to banks and governments? The answer depends on whether customers pay for control, residency and continuity." date: 2026-08-15 updated: 2026-08-16 author: "Philipp D. Dubach" categories: - "AI" keywords: - "sovereign AI" - "Swiss AI infrastructure" - "Swiss data residency" - "local AI inference" - "on-premise AI" - "K3-class inference" - "open-weight models" - "AI infrastructure" - "AI economics" - "private AI deployment" - "DORA" - "AI sovereignty" - "GPU cluster" - "Swiss cloud" - "model hosting" - "data residency" type: "Analysis" canonical_url: "https://philippdubach.com/posts/put-the-model-in-the-basement/" source_url: "https://philippdubach.com/posts/put-the-model-in-the-basement/index.md" content_signal: search=yes, ai-input=yes, ai-train=yes --- # Put the Model in the Basement *Philipp D. Dubach · Published August 15, 2026 · Updated August 16, 2026* ## Key Takeaways - K3-class inference means serving a model similar in scale to Kimi K3. It needs a cluster of 64 graphics processing units (GPUs), not a server under a desk. - The Zurich case assumes $7 million in initial costs, monthly costs of $370,000, and average use of 70%. - At those assumptions, one cluster earns about CHF 7.4 million in annual revenue. EBITDA, a measure of operating profit before financing and non-cash costs, is about CHF 3 million. The cluster repays its initial cost in three years. - The provider sells Swiss residency, audit access, and continuity. The case can fail if use falls, hardware ages, or one cluster has a problem. --- ![A secure local AI server room in a Swiss alpine building. One black server rack stands behind glass beside a window with mountain light.](https://static.philippdubach.com/cdn-cgi/image/width=1600,quality=85,format=auto/mode-basement-cover-image.png) This article follows [I Tried Kimi K3 Inside Claude Code](https://philippdubach.com/posts/kimi-k3-inside-claude-code/). That test asked whether Kimi K3 could work inside a familiar coding setup. Here, I ask what changes when a Swiss provider serves a model of similar scale. This is K3-class inference. Banks already treat data sovereignty as an operating issue. In [How DORA Made Sovereignty a Bank Problem](https://philippdubach.com/posts/dora-critical-cloud-providers-sovereignty/), I argued that banks must plan for concentration, exit, and audit rights. They must also prepare for rule changes by critical foreign providers. AI gets the same treatment. Recent open-weight releases make local use more realistic because operators can download and run their learned model weights. [Kimi K3](https://www.kimi.com/blog/kimi-k3) is huge. Alibaba is moving the Qwen family in the same direction. [Inkling](https://thinkingmachines.ai/news/introducing-inkling/) may matter more. It is a US-trained model with full weights and a focus on customisation. Its one-million-token context window lets it process large amounts of information in one request. ## A Swiss provider: the case Assume a Swiss provider operates 64 graphics processing units (GPUs) as one connected cluster in Zurich. It sells dedicated or managed K3-class inference to banks, pharmaceutical companies, government bodies, and other customers that need Swiss data residency. Their data remain stored and processed in Switzerland. This case assumes $7 million in initial costs, monthly costs of $370,000, and average use of 70%. Subscriptions cost CHF 15,000 to CHF 50,000 each month. With these values, one cluster makes about CHF 7.4 million in annual revenue. The cluster generates about CHF 3 million in earnings before interest, taxes, depreciation, and amortisation (EBITDA). This measure approximates operating profit before financing and non-cash costs. The cluster recovers its initial cost in three years. ![Business case for a Swiss sovereign AI provider. It shows equipment, initial cost, monthly costs, customer mix, five-year results, and sensitivity cases.](https://static.philippdubach.com/cdn-cgi/image/width=1600,quality=85,format=auto/swiss-sovereign-ai-business-case.png) ## The product is control The provider sells control as well as tokens. It offers Swiss residency and a defined security boundary around each customer's data and systems. It does not train on customer data. Customers receive audit access, service-continuity terms, and an exit route if the provider fails. Most companies will not put a model in a basement. Some will pay local providers to get the same control. *Related: [I Tried Kimi K3 Inside Claude Code](https://philippdubach.com/posts/kimi-k3-inside-claude-code/)* ## What can make the case fail A 2.8-trillion-parameter model costs a lot to serve, and one cluster puts every customer on the same equipment. Hardware also loses value quickly. If customers use it less than expected, the business loses money. A smaller model may work almost as well next year, before the operator recovers the cluster's cost. Those risks are real. I still think this market will exist. --- ## Frequently Asked Questions ### What does 'put the model in the basement' mean? It means using a local supplier instead of operating the hardware yourself. The supplier provides local data residency, control, audit access, and continuity. Data residency determines where data are stored and processed. ### Who might buy Swiss sovereign AI inference? The case serves banks, pharmaceutical companies, government bodies, and other customers that need Swiss data residency. ### What does the business case assume? It assumes one Zurich cluster: a connected group of computers with 64 GPUs. The case uses $7 million in initial costs, monthly costs of $370,000, and average use of 70%. Customer subscriptions range from CHF 15,000 to CHF 50,000 each month. ### What can make the case fail? A 2.8-trillion-parameter model costs a lot to serve. One cluster also creates concentration risk because one failure can affect every customer. Hardware loses value quickly, and smaller models may soon work almost as well. --- Canonical: https://philippdubach.com/posts/put-the-model-in-the-basement/ Content-Signal: search=yes, ai-input=yes, ai-train=yes This file is the canonical machine-readable variant of https://philippdubach.com/posts/put-the-model-in-the-basement/. Author: Philipp D. Dubach (https://philippdubach.com/).